My team and I are working on an assignment that provided:
- A model to be tested, consisting of 2 Factors explaining 6 variables; $F1$ would be explained by $X1, X2$ and $X3$, while $F2$ would be explained by $X4, X5$ and $6$;
- Besides that, we are informed about the sample ($N=100$) observations for each $X$;
- The Standard Deviations for each variable;
- We are supposed to test the model fit using CFA;
With that info, we were able to get the covariance matrix and run a CFA through SAS/R without major issues. The thing is: apparently it is important to assume normality to be able to use the ML estimator. What are our alternatives if we didn't want to simply "assume normality"? Are there ways to use different estimators without knowing the means, or the full data set? Thank you immensely for any insights provided.